Abstract

Nonlinear principal component analysis (NLPCA) was used for compression and reconstruction of the total radiance factors (TRFs) of fluorescent samples. The spectral dataset included a total of 358 fluorescent reflectance spectra in the visible range of the spectrum. Spectral data compression was followed by extracting the parameterized nonlinear manifolds using the NLPCA technique. To compare the performance of NLPCA-based compression with the linear method, the orthonormal feature vectors of the dataset were also extracted by the linear PCA. The spectral performance of NLPCA and PCA-based compression approaches was assessed by the root mean square error and the goodness-fitting coefficient between the real and the reconstructed spectra. The percentages of feasible spectra by each method, i.e., those with nonnegative TRFs, were also reported as other criteria for the evaluation of methods. Furthermore, the colorimetric performance of methods were appraised by the measuring the CIELAB 1976 color difference values between the actual and reconstructed spectra under illuminants D65 and A and the 1964 standard observer. The NLPCA-based compression method performed significantly better than the standard PCA-based technique particularly in the lower dimensional spaces of the spectral radiance factors of fluorescent colors.

© 2013 Optical Society of America

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